{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:W7TMHIP2BNJEBZLPBJB2H2RZY3","short_pith_number":"pith:W7TMHIP2","schema_version":"1.0","canonical_sha256":"b7e6c3a1fa0b5240e56f0a43a3ea39c6f53ee4f2969a6177cd98fde6cabfd4e7","source":{"kind":"arxiv","id":"2303.04635","version":1},"attestation_state":"computed","paper":{"title":"Diffusing Gaussian Mixtures for Generating Categorical Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Florence Regol, Mark Coates","submitted_at":"2023-03-08T14:55:32Z","abstract_excerpt":"Learning a categorical distribution comes with its own set of challenges. A successful approach taken by state-of-the-art works is to cast the problem in a continuous domain to take advantage of the impressive performance of the generative models for continuous data. Amongst them are the recently emerging diffusion probabilistic models, which have the observed advantage of generating high-quality samples. Recent advances for categorical generative models have focused on log likelihood improvements. In this work, we propose a generative model for categorical data based on diffusion models with "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2303.04635","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-03-08T14:55:32Z","cross_cats_sorted":[],"title_canon_sha256":"8448a551dae0c04728fb36406fb1e9cef9c32b82265c06d8da739057a211a00f","abstract_canon_sha256":"2b0a95cb99fd5cf5d9fa14b3f0810de8578c396fd290c7d17457f734248e7fee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:49:23.669899Z","signature_b64":"GR+b379bV5sb3laAHh1OeOTXc/xuEJfGHI0Oz8ee4UGWJTfUL5Z2dP1PRPWXt57uBcIKo8wGKWTBqIybro9wBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7e6c3a1fa0b5240e56f0a43a3ea39c6f53ee4f2969a6177cd98fde6cabfd4e7","last_reissued_at":"2026-07-05T05:49:23.669489Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:49:23.669489Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Diffusing Gaussian Mixtures for Generating Categorical Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Florence Regol, Mark Coates","submitted_at":"2023-03-08T14:55:32Z","abstract_excerpt":"Learning a categorical distribution comes with its own set of challenges. A successful approach taken by state-of-the-art works is to cast the problem in a continuous domain to take advantage of the impressive performance of the generative models for continuous data. Amongst them are the recently emerging diffusion probabilistic models, which have the observed advantage of generating high-quality samples. Recent advances for categorical generative models have focused on log likelihood improvements. In this work, we propose a generative model for categorical data based on diffusion models with "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.04635","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2303.04635/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2303.04635","created_at":"2026-07-05T05:49:23.669555+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.04635v1","created_at":"2026-07-05T05:49:23.669555+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.04635","created_at":"2026-07-05T05:49:23.669555+00:00"},{"alias_kind":"pith_short_12","alias_value":"W7TMHIP2BNJE","created_at":"2026-07-05T05:49:23.669555+00:00"},{"alias_kind":"pith_short_16","alias_value":"W7TMHIP2BNJEBZLP","created_at":"2026-07-05T05:49:23.669555+00:00"},{"alias_kind":"pith_short_8","alias_value":"W7TMHIP2","created_at":"2026-07-05T05:49:23.669555+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W7TMHIP2BNJEBZLPBJB2H2RZY3","json":"https://pith.science/pith/W7TMHIP2BNJEBZLPBJB2H2RZY3.json","graph_json":"https://pith.science/api/pith-number/W7TMHIP2BNJEBZLPBJB2H2RZY3/graph.json","events_json":"https://pith.science/api/pith-number/W7TMHIP2BNJEBZLPBJB2H2RZY3/events.json","paper":"https://pith.science/paper/W7TMHIP2"},"agent_actions":{"view_html":"https://pith.science/pith/W7TMHIP2BNJEBZLPBJB2H2RZY3","download_json":"https://pith.science/pith/W7TMHIP2BNJEBZLPBJB2H2RZY3.json","view_paper":"https://pith.science/paper/W7TMHIP2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.04635&json=true","fetch_graph":"https://pith.science/api/pith-number/W7TMHIP2BNJEBZLPBJB2H2RZY3/graph.json","fetch_events":"https://pith.science/api/pith-number/W7TMHIP2BNJEBZLPBJB2H2RZY3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W7TMHIP2BNJEBZLPBJB2H2RZY3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W7TMHIP2BNJEBZLPBJB2H2RZY3/action/storage_attestation","attest_author":"https://pith.science/pith/W7TMHIP2BNJEBZLPBJB2H2RZY3/action/author_attestation","sign_citation":"https://pith.science/pith/W7TMHIP2BNJEBZLPBJB2H2RZY3/action/citation_signature","submit_replication":"https://pith.science/pith/W7TMHIP2BNJEBZLPBJB2H2RZY3/action/replication_record"}},"created_at":"2026-07-05T05:49:23.669555+00:00","updated_at":"2026-07-05T05:49:23.669555+00:00"}